# Discern-XR: An Online Classifier for Metaverse Network Traffic

arXiv: https://arxiv.org/abs/2411.05184 · HTML: https://mlsanity.com/paper/2411.05184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Virtual Reality Gaming on the Cloud: A Reality Check](https://mlsanity.com/paper/2109.10114/) (2021)
- [User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality](https://mlsanity.com/paper/2311.10645/) (2023)
- [Segmented Learning for Class-of-Service Network Traffic Classification](https://mlsanity.com/paper/2208.01793/) (2022)

## Abstract (arXiv)

In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7% while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution.